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Built upon the decision tree (DT) classification and regression idea, thesubspace learning machine (SLM) has been recently proposed to offer higherperformance in general classification and regression tasks. Its performanceimprovement is reached at the expense of higher computational complexity. Inthis work, we investigate two ways to accelerate SLM. First, we adopt theparticle swarm optimization (PSO) algorithm to speed up the search of adiscriminant dimension that is expressed as a linear combination of currentdimensions. The search of optimal weights in the linear combination iscomputationally heavy. It is accomplished by probabilistic search in originalSLM. The acceleration of SLM by PSO requires 10-20 times fewer iterations.Second, we leverage parallel processing in the SLM implementation. Experimentalresults show that the accelerated SLM method achieves a speed up factor of 577in training time while maintaining comparable classification/regressionperformance of original SLM.

Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing